Compact Optimization Learning for AC Optimal Power Flow
This addresses scalability issues for power grid operators using learning-based OPF methods, though it is an incremental improvement over existing approaches.
The paper tackles the scalability problem in end-to-end learning for AC Optimal Power Flow by compressing the solution space with PCA, then learning in this reduced subspace before mapping back to the original output space. This Compact Learning method reduces trainable parameters, achieving significant speed-ups when warm-starting exact solvers on test cases with up to 30,000 buses.
This paper reconsiders end-to-end learning approaches to the Optimal Power Flow (OPF). Existing methods, which learn the input/output mapping of the OPF, suffer from scalability issues due to the high dimensionality of the output space. This paper first shows that the space of optimal solutions can be significantly compressed using principal component analysis (PCA). It then proposes Compact Learning, a new method that learns in a subspace of the principal components before translating the vectors into the original output space. This compression reduces the number of trainable parameters substantially, improving scalability and effectiveness. Compact Learning is evaluated on a variety of test cases from the PGLib with up to 30,000 buses. The paper also shows that the output of Compact Learning can be used to warm-start an exact AC solver to restore feasibility, while bringing significant speed-ups.